www.atmos-chem-phys.net/14/12631/2014/ doi:10.5194/acp-14-12631-2014
© Author(s) 2014. CC Attribution 3.0 License.
Aerosol–computational fluid dynamics modeling of ultrafine and
black carbon particle emission, dilution, and growth near roadways
L. Huang1, S. L. Gong1,*, M. Gordon1, J. Liggio1, R. Staebler1, C. A. Stroud1, G. Lu1, C. Mihele1, J. R. Brook1, and C. Q. Jia2
1Air Quality Research Division, Atmospheric Science and Technology Branch, Environment Canada, Toronto, Ontario, Canada
2Department of Chemical Engineering and Applied Chemistry, University of Toronto, Toronto, Ontario, Canada
*now at: Chinese Academy of Meteorological Sciences 46 Zhong-Guan-Cun S. Ave., Beijing 100081, China
Correspondence to:L. Huang (li.huang@utoronto.ca)
Received: 4 April 2014 – Published in Atmos. Chem. Phys. Discuss.: 14 May 2014 Revised: 17 September 2014 – Accepted: 19 October 2014 – Published: 2 December 2014
Abstract. Many studies have shown that on-road vehi-cle emissions are the dominant source of ultrafine parti-cles (UFPs; diameter < 100 nm) in urban areas and near-roadway environments. In order to advance our knowledge on the complex interactions and competition among at-mospheric dilution, dispersion, and dynamics of UFPs, an aerosol dynamics–computational fluid dynamics (CFD) cou-pled model is developed and validated against field mea-surements. A unique approach of applying periodic bound-ary conditions is proposed to model pollutant dispersion and dynamics in one unified domain from the tailpipe level to the ambient near-road environment. This approach signifi-cantly reduces the size of the computational domain, and therefore allows fast simulation of multiple scenarios. The model is validated against measured turbulent kinetic energy (TKE) and horizontal gradient of pollution concentrations perpendicular to a major highway. Through a model sensi-tivity analysis, the relative importance of individual aerosol dynamical processes on the total particle number concentra-tion (N )and particle number–size distribution (PSD) near a highway is investigated. The results demonstrate that (1) co-agulation has a negligible effect on N and particle growth, (2) binary homogeneous nucleation (BHN) of H2SO4–H2O is likely responsible for elevatedN closest to the road, and (3)Nand particle growth are very sensitive to the condensa-tion of semi-volatile organics (SVOCs), particle dry deposi-tion, and the interaction between these processes. The results
also indicate that, without the proper treatment of the atmo-spheric boundary layer (i.e., its wind profile and turbulence quantities), the nucleation rate would be underestimated by a factor of 5 in the vehicle wake region due to overestimated dilution. Therefore, introducing atmospheric boundary layer (ABL) conditions to activity-based emission models may po-tentially improve their performance in estimating UFP traffic emissions.
1 Introduction
Many studies have shown that vehicle emissions are the dom-inant source of ultrafine particles (UFPs; diameter < 100 nm) in urban areas and near-roadway environments. For example, about 95 % of UFPs (diameter=50∼100 nm) observed near
the health impacts of chemical constituents, such as sulfate, seem to be inconsistent across all epidemiological studies. Comparing epidemiological studies of heart rate variability in humans, Grahame (2009) suggests that differences in ac-curacy of exposure information for health-relevant emissions may explain conflicting study results. This has led to an ur-gent need to study the temporal and spatial variations of local traffic emission in the vicinity of roadways.
With the growing concern of adverse health effects from exposure to UFPs, the gradients of vehicle-emitted pollutants (such as CO, NOx, and UFPs) have been measured in the ambient atmosphere near roadways (e.g., Beckerman et al., 2008; Reponen et al., 2003; Pirjola et al., 2006; Zhu et al., 2009). For example, Zhu et al. (2009) found that elevated particle numbers decay exponentially on the downwind side of three different types of roadways with increasing distance and reach background levels within a few hundred meters. Karner et al. (2010) synthesized field measurements of near-roadway pollutants from over 40 monitoring studies and in-vestigated the concentration–distance relationship. The vari-ation of UFP concentrvari-ations near roadways among studies is likely affected by factors including meteorological con-ditions (wind speed, ambient temperature, relative humidity, and atmospheric stability), traffic characteristics (volume and fleet composition), the geometry of roadways, and aerosol transformation processes (nucleation, coagulation, conden-sation/evaporation, and dry deposition). However, field mea-surements alone are often associated with such limitations as low spatial or temporal resolution in sampling, conclusions restricted by local meteorology, and difficulties in separating the effects of interactive processes.
Therefore, numerical modeling of UFPs has been con-ducted to address these limitations. Due to the challenge of resolving processes with very different scales, a two-stage di-lution modeling strategy, including tailpipe-to-road and road-to-ambient dilutions, has been proposed (Zhang and Wexler, 2004; Zhang et al., 2004). In the first stage (i.e., tailpipe-to-road), strong vehicle-induced turbulence (VIT) results in fast and strong dilution (dilution ratio ∼1000 in 1 s) and
triggers nucleation and condensation/evaporation. While in the road-to-ambient stage, atmospheric boundary layer tur-bulence (ABLT) continues to dilute exhaust particles with ambient air accompanied with particle size changes due to condensation/evaporation. A review study by Carpentieri et al. (2011) has shown that, with recent advances in numeri-cal modeling, computational fluid dynamics (CFD) models can be valuable tools for nanoparticle dispersion in the first stage of dilution. In addition to the limited spatial scale of the dispersion investigated, other limitations in these most recent modeling studies include particle number–size distri-bution (PSD) and chemical composition not being explicitly resolved (Chan et al., 2010). Recent modeling studies of UFP dispersion on street level, on the other hand, have crudely simplified treatment of vehicular emission, VIT, and aerosol dynamics (Gidhagen et al., 2003, 2004a; Kumar et al., 2009).
Most recently, Wang et al. (2013) proposed a two-stage simulation approach to integrate the tailpipe-to-road disper-sion into the road-to-ambient disperdisper-sion stage for the first time. As the authors noted, however, the proposed approach remains computationally demanding, especially when both particle size and chemical composition need to be resolved. To effectively model UFP dynamics and dispersion near roadways in a single unified tailpipe-to-ambient domain, a unique approach of applying periodic boundary conditions to the computational domain is proposed in this paper. Com-pared to a road-to-ambient dispersion modeling approach, the advantage of a unified domain is that the uncertainty due to a simplified or non-existent treatment of VIT can be greatly reduced by explicitly modeling VIT. With VIT being explicitly modeled, aerosol dynamics (such as nucleation, condensation, and evaporation) triggered by the rapid first-stage dilution can be properly incorporated into dispersion models to study their effects on roadside air quality. From a modeling perspective, such a unified model provides a tool to link individual tailpipe emissions (controlled laboratory measurements) to roadside air quality (ambient field mea-surements), which is a noted challenging task (Keskinen and Ronkko, 2010). As the main focus of this paper, we present the development and validation of a multi-component sec-tional aerosol dynamics–CFD coupled model to account for the complex dilution, dispersion, and dynamics of UFPs im-mediately after tailpipe emission to ambient background.
2 Aerosol dynamics–CFD coupled model 2.1 Multi-phase approach to the mixture of
atmospheric gas and aerosol
con-servation equations of mass, momentum and energy with the standardk–εturbulence model.
For the transport of gas and particulate species, Fluent pre-dicts the local mass fraction of each species by solving the advection–diffusion equations. The volume fraction of each secondary phase in a control volume (equivalent to the num-ber concentration of particles of the same size) is obtained by numerically solving the continuity equation for the sec-ondary phase with a specified source term due to aerosol dy-namic processes. The diffusive mass flux in Fluent is mod-eled as the sum of two components: molecular and turbulent diffusion (e.g., Eq. 4 in Di Sabatino et al., 2007). Turbulent diffusion due to VIT and ABLT are the main dilution mecha-nisms for pollutants in the near-road environment (Zhang and Wexler, 2004). The key parameter governing modeled turbu-lent diffusion of pollutants using the RANS approach is the turbulent Schmidt number (Sct), which is defined as the ratio of the turbulent momentum diffusivity and the turbulent mass diffusivity. Analyzing a widely distributed range ofSct(0.2– 1.3) in literature versus the commonly used values (0.7–0.9), Tominaga and Stathopoulos (2007) found that, for plume dis-persion in open country for example, a smaller value ofSct might be used to compensate the underestimated turbulent momentum diffusion. They further suggested adopting its standard value in simulations without this type of underesti-mation. Therefore, given the successful model validation on turbulent kinetic energy (TKE) (discussed in Sect. 4.1.1), the standard value of 0.7 for Sct is used in our study. Thus, the advection, the turbulent mixing, and the diffusion of gases and particles are inherently treated by Fluent through the continuity equation for each phase. Aerosol dynamic pro-cesses, which change the chemical components in particle and gas phases, are integrated through the source terms in continuity equations, and incorporated into Fluent through user-defined functions (UDF).
2.2 Aerosol dynamics
Each secondary phase is a particulate phase composed of mixed chemical components within a specified size range. The density of particles in a given size bin is dynamically computed by Fluent based on the volume-weighted mixing law. From the continuity equation for each secondary phase p, the volume fraction of the secondary phase (αp)is obtained by solving
∂
∂t(αpρp)+ ∇ ·(αpρpu)= −∇ ·(αpρpudr,p)+Sp, (1)
whereuis the velocity field of the primary (gas) phase,udr,p is the drift velocity of secondary phase,Sp=
M P
i=1
Sp,i is the rate of mass transfer for phase p, Sp,i is the rate of mass transfer for species i in phase p, andM is the total num-ber of chemical species in the model. For phase p, the local mass fraction of each species (Yp,i)is predicted by solving
a convection–diffusion equation for the ith species, given
Sp,i due to the aerosol dynamical processes described in Sect. 2.2.1–2.2.4. The total mass of the gas and particulate phases is conserved, while the particle number is diagnosed from the predicted mass. The number concentration of par-ticles in size bin p (Np, in particles/cm3)is computed from the ratio of the phase volume fraction solved by Fluent to the particle volume of a certain size:
Np=10−6·
αp (4/3)π(Dp/2)3
, (2)
whereDpis the diameter (in m) for particles in size bin p. Additionally, the local mass concentration of chemical com-ponenti from particles in size bin p is calculated from the phase volume fraction (αp)and the local mass fraction (Yp,i) as
mp,i=109·αpρpYp,i. (3)
The underlying implementation of aerosol dynamics is a multi-component, size-resolved, sectional aerosol model, as described as follows.
2.2.1 Nucleation
Immediately after tailpipe emissions, new particles form by homogeneous nucleation with initial particle size around 1.5–2.0 nm in the first few milliseconds of exhaust cool-ing and dilution (Kulmala et al., 2007). A qualitative in-vestigation by Zhang and Wexler (2004) found that sulfu-ric acid-induced nucleation could be the dominant new par-ticle production process. The experimental study conducted by Arnold et al. (2006) observed a positive correlation be-tween gaseous sulfuric acid and particle number in the ex-haust of a passenger diesel car burning ultra-low sulfur fuel, indicting an important role of sulfuric acid-induced nucle-ation. The sulfuric acid gas emission rate is estimated based on fuel sulfur content following Uhrner et al. (2007). The parameterization of binary homogeneous nucleation (BHN) of H2SO4–H2O (Vehkamaki et al., 2003) developed specifi-cally for engine exhaust dilution conditions is implemented in this study. This parameterization has already been success-fully used in a number of different aerosol–CFD applications (e.g., Uhrner et al., 2007, 2011; Albriet et al., 2010; Wang and Zhang, 2012).
2.2.2 Coagulation
Particles in the exhaust plume collide due to random (Brow-nian) motion and turbulent mixing to form larger particles, which is called coagulation. The coagulation process reduces
gravitational collection, inertial motion, and turbulent shear. Individual coagulation rate coefficients (or coagulation ker-nels) due to the above driving forces are calculated in this work based upon Jacobson (2005), with consideration of par-ticle flow regimes and convective Brownian diffusion en-hancement. The overall coagulation rate coefficient is the summation of individual coefficients.
2.2.3 Condensation and evaporation
A complex mixture of condensable gases, including water vapor, sulfuric acid, and semi-volatile organics (SVOCs), is emitted from the tailpipe after fuel combustion in the en-gine. During the strong dilution and cooling stage of up to a few seconds after emission (Zhang and Wexler, 2004), super-saturation of these condensable gases occurs and favors the diffusion-limited mass transfer process from the gas phase to the pre-existing particle phase. Following primary emis-sion and nucleation, condensation of SVOCs was suggested by a number of studies (e.g., Clements et al., 2009; Wang and Zhang, 2012; Albriet et al., 2010; Uhrner et al., 2011; Mathis et al., 2004) to be responsible for the rapid growth of nanoparticles in the exhaust plume. As the reverse of densation, evaporation occurs due to further dilution of con-densable gases to subsaturation level in the air surrounding exhaust particles. It was suggested by field measurements of freeway emissions from predominantly gasoline vehicles that lower ambient temperature may favor the condensation of or-ganic species to the particle phase (Kuhn et al., 2005). In this study, the net mass transfer rate of a condensable gas from/to an existing particle with multiple components is driven by the difference between the bulk partial pressure and the sat-uration vapor pressure above the particle surface (Jacobson, 2005). The calculation of species mass transfer rate imple-ments corrections to the diffusion coefficient, the thermal conductivity of air, and the saturation vapor pressure over curved particle surfaces to reflect its dependence on particle size and chemical composition.
2.2.4 Dry deposition
Driven by mechanisms such as Brownian diffusion, turbu-lent diffusion, sedimentation, and advection, dry deposition removes particles at the air–surface interface when they con-tact and remain on the surface (Jacobson, 2005). Brownian diffusion is more effective in removing smaller particles due to their larger diffusion coefficient, while sedimentation is more important for larger particles whose fall speeds are much higher. In the current study, parameterization of par-ticle dry deposition follows the size-resolved dry deposition scheme developed by Zhang et al. (2001). The effect of tur-bulent mixing on particle dry deposition is taken into account by the locally calculated friction velocity. This parameteriza-tion has been successfully validated and implemented in a number of air quality and climate studies (e.g., Gong et al.,
2003; Pye and Seinfeld, 2010), and it has recently been im-proved and extended (Petroff and Zhang, 2010). The recent development accounts for more detailed characteristics of the surface canopy, and suggests possible overestimation of dry deposition velocity for particles in the fine mode. Thus, our current study is likely biased to overestimate the removal of UFPs by dry deposition.
2.3 Modeling turbulence
For turbulence modeling, although the large eddy simulation (LES) approach has been reported to be a more promising so-lution, the standardk–εturbulence model is implemented in this work for several reasons. First, the high computational demands of the LES approach prevent its application for modeling the dispersion and transformation of multiple pol-lutants with complex geometry (i.e., gas and particle emis-sions and aerosol dynamics from multiple vehicles in this study). Compared to RANS closures, the LES approach is at least 1 order of magnitude more computationally expen-sive (Rodi, 1997). Second, a proper treatment of the atmo-spheric boundary layer (ABL) has proven to be crucial to dispersion modeling studies (Blocken et al., 2007b; Zhang, 1994). Recent advances made by Balogh et al. (2012) and Parente et al. (2011a, b) permit a general and practical means to include the ABL using the standardk–εmodel. To achieve this in LES simulations, on the other hand, inflow conditions would have to be carefully generated with additional, sig-nificant computational overhead (Xie and Castro, 2008; Li et al., 2006b). Finally, RANS models agree reasonably well with experimental data in predicting mean flow and pollu-tant concentrations (e.g., Labovsky and Jelemensky, 2011; Sklavounos and Rigas, 2004). Kim et al. (2001) successfully modeled the dispersion of a truck exhaust plume in a wind tunnel usingk–εturbulent closure focusing on rapid dilution and turbulent mixing of exhaust CO2.
Table 1. Median values of the measured meteorological data and model input.
Background PM FEVER measured value Model input
Relative humidity 86.5% 87 %
Ambient temperature (K) 283.65 283.15
3 m wind speed (m s−1) 1.4 1.4
3 m wind direction (degree) 264 260
Friction velocity (m s−1) 0.3 0.3
Monin–Obukhov length (m) 36.9 NA
and Hoxey, 1993) are
u=u ∗
κ ln
z+z 0 z0
(4)
k= u ∗2
p
Cµ
(5)
ε= u
∗3
κ(z+z0)
. (6)
A modified wall function for turbulent mean velocity follow-ing Parente et al. (2011b)
u=u∗ κ ln(E
′
z+′) (7)
is implemented through UDF and applied to wall adjacent cells, where E′= υ
z0u∗ andz
+′=(z+z0)u∗
υ . To keep the de-fault constant value ofσεin the standardk–εmodel, a source term is added to the dissipation rate equation as follows:
Sε(z)=
ρmu∗4
(z+z0)2
(C2−C1)pCµ
κ2 −
1
σε !
. (8)
Furthermore, we adopt the approach by Parente et al. (2011a) to allow a gradual transition from Eqs. (4–6) (i.e the undisturbed ABL) to the wake region simulated by the standardk–εmodel (Supplement, Fig. S4).
3 Simulation setup 3.1 FEVER field study
The Fast Evolution of Vehicle Emissions from Roadway (FEVER) study was conducted to monitor pollutant gradients perpendicular to a major highway north of Toronto, Canada (Highway 400, Hwy 400; 43.994◦
N, 79.583◦
W). The model developed and tested in this paper was designed to simu-late the FEVER observations. A complete description of the monitoring strategies of the FEVER project was documented by Gordon et al. (2012a, b), the BC emission rate for gasoline vehicles was estimated by Liggio et al. (2012), and the rapid organic aerosol production under intense solar radiation was investigated by Stroud et al. (2014).
The site under investigation is a six-lane (25 m across from the lane edges) highway, mainly surrounded by flat agricul-tural fields and some trees lining the side roads, with negli-gible local pollution sources other than vehicular emissions. To validate modeled VIT, the on-road TKE data measured by the Canadian Regional and Urban Investigation System for Environmental Research (CRUISER) mobile laboratory were compared with modeled TKE. The on-road TKE data were measured by two 3-D sonic anemometers during pas-senger vehicle chasing experiments on 6 days between 20 August and 15 September 2010. To validate modeled near-road dispersion, a case study period of 14 and 15 September 2010 between 05:00 and 08:00 LT was chosen for compari-son. The near-road TKE data were measured by a 3-D sonic anemometer at a 3 m tower located 22 m east of the road cen-ter. Wind speed and direction data were measured by an Air-Pointer system (Recordum GmbH), averaged every minute, 34 m east of the road center. As shown in Table 1, the pre-dominant wind direction was approximately perpendicular to the highway and the median Monin–Obukhov length indi-cates near-neutral stability conditions. The CRUISER mobile lab housed instrumentation to measure BC, CO2, and UFPs while driving transects perpendicular to the highway. Follow-ing a previous study (Gordon et al., 2012a), data were filtered for winds within 45◦of the highway normal, which results in removing less than 5 % of the data. In addition, particle size distributions between 14.6 and 661.2 nm were measured at two fixed sites with scanning mobility particle sizer spec-trometers (SMPS) every 3 min and averaged for 05:00–06:00 and 06:00–08:00 LT of 14 and 15 September 2010 for model validation.
3.2 Computational domain and flow boundary conditions
Table 2.Domain sizes for near-road dispersion simulations under different traffic flow conditions.
Scenario Case study period Domain dimensions (x–y–z)
Base case and sensitivity runs 06:00–08:00 LT 375 m×48 m×50 m Half traffic case 05:00–06:00 LT 375 m×91 m×50 m
2
(b) (b)
3
Figure 1. Computational domain (a) and running vehicles and ground mesh(b). Purple mesh indicates velocity-inlet boundaries (left and top); red mesh indicates pressure-outlet boundary (right); black mesh indicates wall boundaries (bottom and cars); and cyan mesh indicates translational periodic boundaries (lateral).
on the measured traffic volume of about 104.3 passenger ve-hicles min−1, traveling speed of approximately 120 km h−1 (or 33.3 m s−1), and the assumed average vehicle length of 4.5 m, the average y axis distance (bumper to bumper) be-tween two vehicles traveling in adjacent lanes is calculated as 11.5 m assuming all three lanes are evenly occupied. Thus, the horizontal dimension of 48 m along the highway (yaxis) is calculated based on the measured traffic volume for week-day early morning rush hours between 06:00 and 08:00 LT. The whole domain is meshed into 871 065 unstructured hex-ahedral cells with the finest ones concentrated around the moving vehicles, tailpipes, and their wake regions and im-materially above the ground.
In our simulation, the vehicles are set to be stationary, nonslip walls with a roughness length of 0.0015 m (Wang and Zhang, 2009), while the blowing air has two velocity components: the first component towards the vehicles (or the
Table 3. Background concentrations of particulate and gaseous species considered in the model.
Background PM FEVER measured value Model input
BC (µg m−3) 0.298–0.53 0.39
OA (µg m−3) 0.676–1.50 1.04
PM2.5(µg m−3) ∼5.0 4.78
N(no./cm3) 4921–7335 5800
CO2(ppmv) 412.7–421.3 415
3.3 Chemical boundary conditions: background concentrations and traffic emissions
In addition to meteorological and traffic data, chemical data of gases and particles are required as part of CFD bound-ary conditions. According to the source type, the required chemical data are divided into two categories: background concentrations and traffic emission rates. The mass concen-trations of background gaseous and particulate species from the FEVER field measurements are listed in Table 3, with their corresponding values used as model input. The back-ground gas phase includes dry air (O2 and N2), water va-por, and CO2. The CO2volume fraction and relative humid-ity (RH) value are converted to mass fractions to specify the species input values for the velocity-inlet boundaries. For the particulate phase, a bi-modal lognormal particle size distri-bution is assumed (as summarized in Table 5). The param-eters of background particles are obtained from the FEVER measurements at about 100 m upwind of Hwy 400. Given the total backgroundN and PSD, the volume fractions of indi-vidual size bins are obtained, and the mass of black carbon (BC) and organic aerosol (OA) is distributed into each size bin according to the ratio of their background mass concen-trations listed in Table 3.
Vehicles driving on the highway continuously emit a com-plex mixture of gases and particles. It is not possible to in-clude a complete set of gaseous and particulate species in the model, which also is not numerically practical. In this study, the tailpipe emission rates of the gaseous and partic-ulate species are summarized in Table 4. Currently, the ex-haust gas is composed of CO2, H2O, H2SO4, SVOCs, and N2, which are key species to the aerosol dynamics and dis-persion. The treatment of H2SO4 as direct emission rather than a mixture of SO2and SO3followed by hydrolysis has been explained in Sect. 2.2.1. It was suggested by model-ing smodel-ingle exhaust plumes (e.g., Albriet et al., 2010; Uhrner et al., 2007, 2011) that SVOCs are likely to be responsible for the rapid growth in particle size when they condense on UFPs. Following Albriet et al. (2010), pyrene (C16H10),N nonadecane (C19H40), andNpentacosane (C25H52)are intro-duced to represent the polycyclic semi-volatile organic com-pounds, the semi-volatile alkanes between C14and C22, and semi-volatile alkanes between C23and C29, respectively. The mass fractions of the above three groups of SVOCs are based on Albriet et al. (2010), and the total mass emission rate of SVOCs is set as 0.0186 g km−1(Pye and Seinfeld, 2010). All SVOCs initially from the tailpipe are assumed to exist only in the gas phase, but are subject to interactions with the parti-cle phase through condensation/evaporation upon immediate dilution with the surrounding air. To reduce the number of species considered in the model, the non-volatile fraction of primary organic aerosol (POA) from tailpipes is assumed to share the properties of the background OA, i.e., with an av-erage molecular mass of 300 g mol−1 and an average den-sity of 1.5 g cm−3. This assumption is not likely to affect
our results because the amount of the non-volatile fraction of POA from tailpipes is very small compared to the back-ground OA.N and PSD for tailpipe emissions are based on a recent study by Nikolova et al. (2011b), which provides an emission rate according to traffic volume and type. As pointed out by Nikolova et al. (2011a), however, the param-eterization they originally proposed implicitly accounts for a fast nucleation process. As indicated by laboratory measure-ments (Ronkko et al., 2007; Kirchner et al., 2009), the nucle-ation mode particles have a nonvolatile core in the exhaust of a heavy-duty diesel vehicle; however, they are completely volatile under 280◦C in the exhaust of a diesel passenger car. Thus, we assume in this study thatN of nucleation mode particles from all passenger cars are from BHN, while those from heavy-duty vehicles (HDVs) have a solid core of BC and nonvolatile POA. Given the mass flow rate of the tailpipe exhaust, the mass fraction of each individual species can be estimated from its mass emission rate listed in Table 4. These mass fractions are used to specify chemical boundary condi-tions for tailpipes.
4 Results and discussions
Turbulent mixing of tailpipe emissions with the ambient air largely determines the initial dilution and the three-dimensional distribution of the traffic pollutants downwind of roadways. Thus, the modeled TKE is first compared against on-road and near-road TKE measurements reported by Gordon et al. (2012b). For model validation results in Sect. 4.1, two scenarios with different traffic conditions (base case: 06:00–08:00 LT and half traffic case: 05:00–06:00 LT) are considered. The modeled CO2 and BC concentrations and PSDs are compared with the FEVER field measure-ments. Finally, the impacts of individual aerosol dynamical processes on UFPs and model sensitivity to the treatment of ABL are investigated in Sect. 4.2. A total of five sensi-tivity runs are performed based upon the base case (06:00– 08:00 LT). Four sensitivity runs are conducted by turning off a single aerosol dynamical process for each run, and the re-sults are compared with the base case in Table 6. An addi-tional sensitivity run is conducted without maintaining mod-eled ABL profiles through Eqs. (7–8).
4.1 Model validation
4.1.1 Turbulent kinetic energy
Table 4.Tailpipe gaseous and particulate species mass emission rates (g km−1driven) for gasoline engines.
Species Reference emission rates Reference Model input
(g km−1driven) (g km−1driven)
CO2 278 Estimated from fuel-based emission factors∗ 278
and the observed vehicle composition
SVOCs 0.0186 Gasoline-powered vehicles 0.0186
(Pye and Seinfeld, 2010)
H2SO4 2.94–8.82×10−3 Light-duty diesel vehicles 6.25×10−5 (Uhrner et al., 2007)
0–3.4×10−7 Light-duty gasoline vehicles (Seigneur, 2009)
H2O 99 Light-duty diesel vehicles 99
(Uhrner et al., 2007)
BC 0.0063 SP2 data (Liggio et al., 2012) 0.0063
Non-volatile POA 0.0020 MOBILE6.2C 0.0020
∗Canada’s National Inventory Report 1990–2009.
Table 5.Particle number–size distribution parameters assumed by the model.
Sampling site Particle mode Number Geometric σmean
(no./cm3) concentration diameter (nm)
Background Soot mode 5000 50 1.6
Accumulation mode 800 120 1.6
Tailpipe (raw exhaust) Nucleation mode 3.86×107 15 1.4
Soot mode 9.42×106 60 1.6
0 2 4 6 8 10
1 2 3 4 5 6
TK
E
m
2/s
2
Time (s)
Measured PC (V=20 m/s) Modeled Sedan (V=15 m/s) Modeled SUV (V=20 m/s) Modeled SUV (V=25 m/s)
Figure 2.Comparison of the on-road TKE from the passenger ve-hicle chasing experiments of the FEVER project (black line) and model simulations (red, blue, and purple lines). The error bars rep-resent the 25th and 75th percentiles of the measured on-road TKE. PC stands for passenger vehicle and SUV stands for sport utility vehicle. V is the average traveling speed (m s−1) of PC in chasing experiments or the vehicle speed used in model simulation.
passenger cars and they axis is the 10 s average TKE. The modeled TKE values in Fig. 2 are calculated for individual vehicle wakes super-imposed on an estimated background
on-road TKE of 2.4 m2/s2(Gordon et al., 2012b). The aver-age traveling speed during the FEVER chasing experiments was about 20 m s−1, and 84.5 % of the measurements were taken at a chasing speed between 15 and 25 m s−1. Therefore, model simulations are conducted for passenger cars traveling at 15, 20, and 25 m s−1. As shown in Fig. 2, the modeled TKE in the wake of a vehicle traveling at a speed of 15–25 m s−1 agrees well within the 25th and 75th percentile of the mea-surements; furthermore, the variations among modeled TKE in Fig. 2 show the sensitivity of on-road TKE to vehicle type and traveling speed. With these scenarios agreeing within the 25th and 75th percentile of the measurements, it is clear that the turbulent mixing within individual vehicle wakes on the highway can be reasonably well modeled.
Table 6.Number concentration and mean diameter of the nucleation mode particles predicted by the model under different scenarios.
Sampling site
Base case
Without deposition
Without condensation
Without nucleation
Without coagulation
Number concentration (no./cm3)
B 5.94×104
(NA)
7.29×104 (−23 %)∗
6.29×103 (89 %)
3.77×104 (36 %)
5.79×104 (2 %)
C 1.59×104
(NA)
2.44×104 (−53 %)
9.63×102 (94 %)
1.42×104 (10 %)
1.64×104 (−3 %)
Geometric mean diameter (nm)
B 19.8 18.8 15.5 20.2 19.5
C 24.3 19.6 15.2 25.4 23.3
∗Normalized bias (in percentage) was calculated as (base case – scenario)/base case×100 %.
0 50 100 150 200 250 300
0.0 0.5 1.0 1.5 2.0 2.5
TK
E (
m
2 /s
2 )
Distance from Hwy-400 (m)
Measured (05-06am) Modeled (05-06am) Measured (06-08am) Modeled (06-08am)
Figure 3.Comparison of the TKE from the FEVER observations at a roadside tower and model simulations for morning rush hour: 05:00–06:00 (red) and 06:00–08:00 LT (blue) (measurements in im-age referred to in a.m.). Measurement data are plotted as solid lines and model simulation results are plotted as dashed lines. The error bars represent 25th to 75th percentiles.
investigated based on measurements at a fixed location in a Lagrangian sense. There are 120 and 240 measurements of TKE taken for the periods of 05:00–06:00 and 06:00– 08:00 LT, respectively, and they are binned by distance as shown in Fig. 3. The obtained wind trajectory distances vary between 20 and 80 m with about 93 % of them concentrating on the first bin (20–40 m). For the period of 05:00–06:00 LT, the measured (modeled) TKE at a distance of 20–40 m from the highway center is in the range of 0.46–0.80 (0.58–0.73) m2s−2. Similarly, for the period of 06:00–08:00 LT, the mea-sured (modeled) TKE lies in the range of 0.55–0.90 (0.65– 0.95) m2s−2. Although the observed TKE decay is limited in spatial resolution for both time periods, the comparison in
Fig. 3 shows an adequate agreement with the field measure-ments and suggests turbulent mixing in a roadside environ-ment can be successfully modeled even with varying traffic volumes.
4.1.2 Near-road concentration gradients: CO2and BC As a chemically passive gas species in vehicular emissions, CO2 is an ideal indicator of atmospheric mixing of tailpipe exhaust with ambient air. In a previous study (Kim et al., 2001), CO2was experimentally measured inside a single tur-bulent plume of heavy-duty truck exhaust and successfully modeled with the standardk–εmodel in the CFD code Flu-ent. The focus of this study, however, is the horizontal con-centration gradient on the downwind side of a highway.
Figure 4a shows the concentration of CO2 (ppmv) as a function of downwind distance from the center of Hwy 400 for the morning period of 06:00–08:00 LT. FEVER mea-surements were first corrected to wind trajectory distance, grouped into 20 m bins between 50 and 350 m, and then plot-ted in median concentrations and 25th and 75th percentiles. Modeled CO2concentrations closely follow the decreasing trend of the median values of the FEVER measurements, and agree well within the 25th and 75th percentiles. How-ever, the model tends to underestimate CO2 concentrations by about 6 ppmv in the first 50 m (i.e., 50–100 m) of down-wind distance and overestimate by about 8 ppmv in the last 50 m (i.e., 250–300 m). Similarly, the concentration–distance relationship for particulate BC is shown in Fig. 4b. Modeled BC concentrations are also within the 25th and 75th per-centiles exhibiting a trend with distance similar to the median of the measured values. Similar to CO2, minor underestima-tions (15 %) between 50 and 100 m from Hwy 400 and slight overestimations (20 %) after 100 m were observed for partic-ulate BC.
underesti-0 50 100 150 200 250 300 350 410
415 420 425 430 435 440
445 FEVER (25th and 75th percentiles)
FEVER (Median) Model
CO
2
(p
p
m
v
)
Distance from Hwy-400 (m)
0 50 100 150 200 250 300 350
0.0 0.2 0.4 0.6 0.8 1.0 1.2
1.4 FEVER (25
th
and 75th percentiles)
FEVER (Median) Model
BC (
g/
m
3 )
Distance from Hwy-400 (m)
Figure 4.Comparison of modeled and measured near-road concen-trations of CO2(parts per million volume, ppmv) and BC (µg m−3) on the downwind side of Hwy 400. Median concentrations from FEVER measurements are plotted in black solid lines, and mod-eled concentrations are plotted in red dashed lines. The gray areas represent measurements within 25th and 75th percentiles.
mated vertical mixing by the model. Beyond 100 m from the road, vertical diffusion of the near-surface pollutants results in the overestimations. There are two factors that may ex-plain the underestimated vertical mixing closest to the road. First, the modeled road structure is missing a 1 m high bar-rier at the highway center, which could potentially lift near-surface pollutants under cross-wind conditions (Ning et al., 2010; Hagler et al., 2011). Second, midsize and heavy-duty trucks are neglected due to their small fractions in total traf-fic, which emit pollutants at a greater height (up to 4 m) than (0.5 m) passenger cars (Gordon et al., 2012a).
10 100 1000
0
2x104
4x104
6x104
8x104
Site B
dN/dlogDp (part
icles/cm
3 )
Dp (nm)
SMPS (6-8am) SMPS (5-6am) Model (6-8am) Model (5-6am)
3
10 100 1000
0.0
4.0x103
8.0x103
1.2x104
dN/dlogDp (part
icles/cm
3 )
Dp (nm)
SMPS (6-8am) SMPS (5-6am) Model (6-8am) Model (5-6am)
Site C
4
Figure 5.Particle number–size distributions at site B (34 m from the highway center) and site C (300 m from the highway center).
4.1.3 Particle size distribution
in-coming solar radiation, and wind velocity) remained approx-imately constant.
Comparing the measured PSDs at two sites, it was found that, for all measured particle sizes, the number concentra-tions decreased significantly when particles were transported from 34 to 300 m downwind of the highway. The observed total particle number concentrations decreased by a factor of about 2.5 and 5.7 between these two locations for the peri-ods of 05:00–06:00 and 06:00–08:00 LT, respectively. Sim-ilar to many previous roadside monitoring studies reviewed by Pant and Harrison (2013), the measured PSDs showed dis-tinct multi-modal size regimes (i.e., nucleation, soot, and ac-cumulation modes) in the measured size range of 15–700 nm. Tri-modal lognormal curve fitting of the observed PSDs re-vealed a nucleation mode at 20–25 nm, a soot mode at 65– 75 nm, and an accumulation mode at 160–380 nm. In agree-ment with Zhu et al. (2002a, b), the nucleation mode parti-cles dominatedN and decreased much faster (by a factor of 8 in this study) than that of the accumulation particles (only a factor of 2 in this study). It was also found that the geometric mean diameter of the nucleation mode increased from 20.0 (at site B) to 24.7 nm (at site C), which may be attributed to the condensation of SVOCs (Clements et al., 2009) and the coagulation of nucleation mode particles (Zhu et al., 2002b). The comparison in Fig. 5 demonstrates an adequate agree-ment between the modeled and the observed PSD at both dis-tances under different traffic conditions. For the peak traffic hours during 06:00–08:00 LT, the model estimated total par-ticle number concentrations are 6.25×104 and 1.66×104
particles cm−3at sites B and C, respectively, with approxi-mately 10 % underestimations compared to the observations. Second, the dominant nucleation mode was properly cap-tured by the current model, as well as its decreasing trend with increasing distance from the highway. Furthermore, the nucleation mode particles were modeled to grow from 19.8 to 24.3 nm in geometric mean diameter with increasing distance away from the highway. This agrees exceptionally well with the observations. Similar conclusions can be drawn from the 05:00–06:00 LT comparison.
However, the model clearly underestimated the number concentrations of particles of 100–730 nm in diameter. This discrepancy can be attributed, at least partially, to the missing non-tailpipe emissions in the current model, such as brake wear, road–tire interaction, and re-suspension of road dust as reviewed by Kumar et al. (2013). Although road dust parti-cles formed mechanically by frictional contact between road surface and tire or between break system components are as-sumed to be primarily coarse particles, both laboratory ex-periments (Dahl et al., 2006; Gustafsson et al., 2008) and real-world measurements (Mathissen et al., 2011) recently observed a significant portion of particles to be in the range of 6–700 nm in diameter. On the other hand, the estimated emis-sion factors for sub-micrometer particles generated by the road–tire interaction under steady driving condition based on these studies vary significantly, indicating that the
emis-sion strength tends to be very site specific. Thus, the under-estimated particles larger than 100 nm might be a result of missing estimates of non-tailpipe emissions for the underly-ing site.
4.2 Model sensitivity analysis
4.2.1 Role of aerosol dynamical processes
Along with dilution, aerosol dynamical processes (i.e., con-densation/evaporation, coagulation, nucleation, and dry de-position) may interact with one another and modifyN and PSD in near-road environments. In this section, the relative importance of the above aerosol dynamical processes is in-vestigated by conducting simulations with individual pro-cesses removed and comparing against the base case simu-lation, in which all dynamical processes are considered by the model. The obtainedN and the geometric mean diam-eter of nucleation mode particles from this sensitivity anal-ysis are summarized in Table 6. The base case simulation demonstrates that in moving from site B to site C, the nucle-ation mode particles decrease by approximately a factor of 3, and the geometric mean diameter increases by 4.5 nm. The modeled soot mode and accumulation mode particles are ex-cluded from the analysis due to significant underestimations compared to the measurements, as discussed in the previous section. The results of excluding particle dry deposition pro-cess are investigated first because its impact onN can inter-act with particle nucleation and condensation processes, as discussed later in this section.
When particle dry deposition is deactivated in the model, nucleation mode particle numbers increase significantly (∼23 and 53 % at site B and C, respectively), resulting in 1–5 nm smaller geometric mean diameters compared to the base case as listed in Table 6. The modeled particle dry de-position velocity is up to 0.2 m s−1for the smallest particles of 3–5 nm in diameter due to strong Brownian diffusion. Our results show that particle dry deposition plays a significant role in governingN in the vicinity of roadways between 30 and 300 m. Gidhagen et al. (2004b) estimated that dry de-position removes only about 12 % of total particles near a Swedish highway, in contrast to our estimation of 15–35 %. This discrepancy may be due to the different treatment of atmospheric boundary layer turbulence in both studies. Gid-hagen et al. (2004b) introduced an artificial source of tur-bulence into their model to mimic the observed atmospheric dilution of NOxnear the road, while the theoretically based method by Parente et al. (2011b) combined with the mea-sured ABLT was implemented in this study. The modeled VIT and ABLT have been validated against the measure-ments in Sect. 4.1.1.
without condensation are about 1 order of magnitude lower than the base case, and are the lowest among all scenarios. The implication of this is twofold. In agreement with previ-ous modeling studies (i.e., Wang and Zhang, 2012; Uhrner et al., 2007; Uhrner et al., 2011; Albriet et al., 2010), it strongly suggests that the condensation of SVOCs is responsible for the growth of nucleation mode particles during their atmo-spheric transport. It also reflects the strong interactions be-tween particle growth and removal processes in governing the simulated particle number. Without the condensational growth of nucleation mode particles, new particles formed due to the BHN mechanism remain in the smallest size bin of 3–5 nm in diameter. Immediately after formation, these par-ticles are subject to efficient removal by dry deposition due to their small particle sizes, resulting in the lowestNamong scenarios. This result implies that controlling tailpipe SVOC emissions may indirectly help reduce UFP number concen-trations in the vicinity of roadways.
For the scenario without BHN of H2SO4–H2O, the geo-metric mean diameters at both sites are similar to the base case with slightly more condensational growth in size. How-ever, the particle number concentrations are underestimated by 36 and 10 % at site B and C, respectively, compared to the base case. This implies that over 60 and 90 % of the nucle-ation mode particles at site B and C are attributed to HDV emissions with non-volatile cores. The result also shows that the BHN of H2SO4–H2O has the greatest impact on the par-ticle population closest to the road. This is because the parti-cles formed through BHN are much smaller in size than those directly emitted with non-volatile cores around 15 nm in di-ameter. Thus, particles of BHN origins are subject to faster dry deposition removal, and contribute less to N at greater distances in the near-road environment. However, it should not be ignored in air quality modeling studies of mobile emis-sions, especially within the first 100 m of the roadways.
The scenario excluding particle coagulation alone results in the least impact on both N and geometric mean diam-eter of nucleation mode particles near the road. The re-sults strongly agree with both timescale analysis (Zhang and Wexler, 2004) and previous CFD modeling studies (Wang and Zhang, 2012; Albriet et al., 2010; Gidhagen et al., 2004b). However, the coagulation process was suggested to be important under mild to weak atmospheric dilution con-ditions, such as street canyons (Gidhagen et al., 2004a) and road tunnels (Gidhagen et al., 2003).
4.2.2 Role of atmospheric boundary layer
Previous studies have shown that accurate CFD simulation of the ABL (including its wind profile and turbulence quan-tities) is essential for atmospheric dispersion of inert pollu-tants. For example, Gorle et al. (2009) investigated the effect of atmospheric TKE on the dispersion of particles of 1 µm in diameter, and concluded the impact was significant. In their study, however, aerosol dynamical processes were not
con-0 50 100 150 200 250 300
2x104
4x104
6x104
8x104
UFP
(p
articles/cm
3)
Distance from Hwy-400 (m) Base case Without ABL SMPS (fixed locations) (a)
2
0 50 100 150 200 250 300
420 425 430 435 440 445
CO
2
(ppmv)
Distance from Hwy-400 (m) Base case Without ABL (b)
3
Figure 6.Predicted UFP number(a)and CO2(b)concentrations as a function of distance to the center of Hwy 400.
sidered, nor were their interactions with the ABLT. Here, a sensitivity analysis on the ABL is performed to investigate the impact of the ABL on UFP formation and dispersion in the near-roadway environment. Specifically, the base case simulation is compared with a test simulation where only the wall-function modifications of Eqs. (7)–(8) are not applied.
Figure 6a shows the model predicted UFP concentrations of the base case and the test simulations, along with the in-tegration of SMPS data at two fixed locations for the 06:00– 08:00 LT morning rush hours. The predicted concentrations of UFPs from the test simulation are lower by about 1×104 particles/cm3(or 18 % of the background corrected peak con-centration) near the center of the highway compared to the base case. However, the concentration difference for CO2 (as shown in Fig. 6b) between the two simulations is only slightly different (∼10 % of its background corrected peak
Thus, the concentration underestimation for CO2is the result of the overestimated dilution near the surface, where vehicu-lar exhaust occurs.
In addition to the overestimated dilution effect on par-ticle dispersion, the impact of the ABL on UFP num-ber concentrations is enhanced by the reduced nucleation rate due to the underestimation of gaseous precursors (i.e., H2SO4 and H2O) in the vehicle wake regions. The maximum nucleation rate from both simulations is around 2.9×1016particles m−3s−1, which are in the range of 1–
6.3×1016particles/m3/s from single vehicle exhaust plume
simulations (Wang and Zhang, 2012; Uhrner et al., 2007). Al-though the maximum nucleation rate is not sensitive to ABL profiles, the area-averaged nucleation rate of the cross sec-tion in the exhaust pipe plane behind the vehicle is underes-timated by a factor of 5 due to the overesunderes-timated dilution be-hind vehicles in the sensitivity run. This comparison strongly suggests that the concentration of UFPs from mobile sources may be even more sensitive to the ABL conditions than inert gaseous species. It also implies that introducing ABL con-ditions to activity-based emission models (such as Nikolova et al., 2011b) may potentially improve their performance in estimating UFP traffic emissions.
5 Conclusions
In this study, an aerosol dynamics–CFD coupled model is applied to a single unified computational domain to investi-gate the dynamics and dispersion of UFPs from tailpipe ex-haust to the near-road environment. The interactions among individual exhaust plumes are explicitly modeled within the tailpipe-to-ambient computational domain. The unique ap-plication of translational periodic boundary conditions effec-tively reduces the size of the computational domain and al-lows fast multiple-scenario simulations of size-resolved and chemical-component-resolved aerosol dynamics. This paper has demonstrated that, together with field measurements, the model is an effective tool which can be used to advance our knowledge on the formation and dispersion of UFPs in the near-road environment. This information is needed to help develop parameterizations of sub-grid processes ultimately to improve air quality model simulations over urban areas.
The model was successfully validated with FEVER field study measurements of both on-road and near-road TKE. The results indicate that the strength of turbulent mixing of pollutants due to VIT and the ABLT is properly captured by the model, leading to good agreement between modeled and measured concentrations for CO2 and BC. For UFPs, the modeled PSDs demonstrated adequate agreement with measurements at two fixed locations near a major highway, under different traffic conditions. Sensitivity analysis indi-cated that the modeled N and PSD of UFPs are sensitive to H2SO4–H2O binary homogeneous nucleation, conden-sation/evaporation of SVOCs, and particle dry deposition. However, for such an unconfined near-road environment as in this study, coagulation appears to have a negligible ef-fect on UFPs. Results also suggest that UFPs from mobile sources may be even more sensitive to ABL conditions than inert species because the average nucleation rate in vehicle wakes is very sensitive to the dilution of H2SO4. Therefore, introducing ABL conditions to activity-based emission mod-els may potentially improve their performance in estimating UFP traffic emissions.
Appendix A
Table A1.Nomenclature.
u ABL mean velocity
k Turbulent kinetic energy or TKE
ε Dissipation rate of TKE
u∗ Friction velocity
κ Von Karman constant
z Height above the ground
z0 Aerodynamic roughness length Cµ Constant in the standardk–εmodel σε Turbulent Prandtl number forε E′ Wall function constant
z+′ Non-dimensional wall distance
The Supplement related to this article is available online at doi:10.5194/acp-14-12631-2014-supplement.
Acknowledgements. This research was supported though the
Program of Energy Research and Development (PERD) under the specific Particle and Related Emission projects C11.008 and C12.007. PERD is a program administered by Natural Resources Canada. The authors would also like to thank Professor Alessandro Parente at Université Libre de Bruxelles and his colleagues for providing CFD code for modeling the ABL in Fluent.
Edited by: R. MacKenzie
References
Albriet, B., Sartelet, K. N., Lacour, S., Carissimo, B., and Seigneur, C.: Modelling aerosol number distributions from a vehicle ex-haust with an aerosol CFD model, Atmos. Environ., 44, 1126– 1137, doi:10.1016/j.atmosenv.2009.11.025, 2010.
Alinot, C., and Masson, C.: k-epsilon Model for the atmospheric boundary layer under various thermal stratifications, J. Sol. En-ergy Eng. Trans.-ASME, 127, 438–443, doi:10.1115/1.2035704, 2005.
Arnold, F., Pirjola, L., Aufmhoff, H., Schuck, T., Lahde, T., and Hameri, K.: First gaseous sulfuric acid mea-surements in automobile exhaust: Implications for volatile nanoparticle formation, Atmos. Environ., 40, 7097–7105, doi:10.1016/j.atmosenv.2006.06.038, 2006.
Balogh, M., Parente, A., and Benocci, C.: RANS simulation of ABL flow over complex terrains applying an Enhanced k-epsilon model and wall function formulation: Implementation and com-parison for fluent and OpenFOAM, J. Wind Eng. Ind. Aerodyn., 104, 360–368, doi:10.1016/j.jweia.2012.02.023, 2012.
Beckerman, B., Jerrett, M., Brook, J. R., Verma, D. K., Arain, M. A., and Finkelstein, M. M.: Correlation of nitrogen dioxide with other traffic pollutants near a major expressway, Atmos. Envi-ron., 42, 275–290, doi:10.1016/j.atmosenv.2007.09.042, 2008. Blocken, B., Carmeliet, J., and Stathopoulos, T.: CFD evaluation
of wind speed conditions in passages between parallel buildings - effect of wall-function roughness modifications for the atmo-spheric boundary layer flow, J. Wind Eng. Ind. Aerodyn., 95, 941–962, doi:10.1016/j.jweia.2007.01.013, 2007a.
Blocken, B., Stathopoulos, T., and Carmeliet, J.: CFD simulation of the atmospheric boundary layer: wall function problems, Atmos. Environ., 41, 238–252, doi:10.1016/j.atmosenv.2006.08.019, 2007b.
Brugge, D., Durant, J. L., and Rioux, C.: Near-highway pollutants in motor vehicle exhaust: A review of epidemiologic evidence of cardiac and pulmonary health risks, Environ. Health, 6, 12, doi:10.1186/1476-069x-6-23, 2007.
Carpentieri, M., Kumar, P., and Robins, A.: An overview of ex-perimental results and dispersion modelling of nanoparticles in the wake of moving vehicles, Environ. Pollut., 159, 685–693, doi:10.1016/j.envpol.2010.11.041, 2011.
Chan, T. L., Liu, Y. H., and Chan, C. K.: Direct quadrature method of moments for the exhaust particle formation and evolution in the wake of the studied ground vehicle, J. Aerosol. Sci., 41, 553– 568, doi:10.1016/j.jaerosci.2010.03.005, 2010.
Clements, A. L., Jia, Y. L., Denbleyker, A., McDonald-Buller, E., Fraser, M. P., Allen, D. T., Collins, D. R., Michel, E., Pudota, J., Sullivan, D., and Zhu, Y. F.: Air pollutant concentrations near three Texas roadways, part II: Chemical characterization and transformation of pollutants, Atmos. Environ., 43, 4523–4534, doi:10.1016/j.atmosenv.2009.06.044, 2009.
Dahl, A., Gharibi, A., Swietlicki, E., Gudmundsson, A., Bo-hgard, M., Ljungman, A., Blomqvist, G., and Gustafsson, M.: Traffic-generated emissions of ultrafine particles from pavement-tire interface, Atmos. Environ., 40, 1314–1323, doi:10.1016/j.atmosenv.2005.10.029, 2006.
Di Sabatino, S., Buccolieri, R., Pulvirenti, B., and Britter, R.: Sim-ulations of pollutant dispersion within idealised urban-type ge-ometries with CFD and integral models, Atmos. Environ., 41, 8316–8329, doi:10.1016/j.atmosenv.2007.06.052, 2007. Franke, J.: Best practice guideline for the CFD simulation of flows
in the urban environment, Meteorological Inst., 2007.
Gidhagen, L., Johansson, C., Strom, J., Kristensson, A., Swietlicki, E., Pirjola, L., and Hansson, H. C.: Model simulation of ultrafine particles inside a road tunnel, Atmos. Environ., 37, 2023–2036, doi:10.1016/s1352-2310(03)00124-9, 2003.
Gidhagen, L., Johansson, C., Langner, J., and Olivares, G.: Sim-ulation of NOx and ultrafine particles in a street canyon in Stockholm, Sweden, Atmos. Environ., 38, 2029–2044, doi:10.1016/j.atmosenv.2004.02.014, 2004a.
Gidhagen, L., Johansson, C., Omstedt, G., Langner, J., and Oli-vares, G.: Model simulations of NOxand ultrafine particles close to a Swedish highway, Environ. Sci. Technol., 38, 6730-6740, 10.1021/es0498134, 2004b.
Gong, S. L., Barrie, L. A., Blanchet, J. P., von Salzen, K., Lohmann, U., Lesins, G., Spacek, L., Zhang, L. M., Girard, E., Lin, H., Leaitch, R., Leighton, H., Chylek, P., and Huang, P.: Cana-dian Aerosol Module: A size-segregated simulation of atmo-spheric aerosol processes for climate and air quality models – 1. Module development, J. Geophys. Res.-Atmos., 108, 16, doi:10.1029/2001jd002002, 2003.
Gordon, M., Staebler, R. M., Liggio, J., Li, S.-M., Wentzell, J., Lu, G., Lee, P., and Brook, J. R.: Measured and modeled variation in pollutant concentration near roadways, Atmos. Environ., 57, doi:10.1016/j.atmosenv.2012.04.022, 2012a.
Gordon, M., Staebler, R. M., Liggio, J., Makar, P., Li, S.-M., Wentzell, J., Lu, G., Lee, P., and Brook, J. R.: Measurements of Enhanced Turbulent Mixing near Highways, J. Appl. Meteorol. Climatol., 51, doi:10.1175/jamc-d-11-0190.1, 2012b.
Gorle, C., van Beeck, J., Rarnbaud, P., and Van Tende-loo, G.: CFD modelling of small particle dispersion: The influence of the turbulence kinetic energy in the atmo-spheric boundary layer, Atmos. Environ., 43, 673–681, doi:10.1016/j.atmosenv.2008.09.060, 2009.
Grahame, T. J.: Does improved exposure information for PM2.5 constituents explain differing results among epi-demiological studies?, Inhal. Toxicol., 21, 381–393, doi:10.1080/08958370802380495, 2009.
Proper-ties and toxicological effects of particles from the interaction be-tween tyres, road pavement and winter traction material, Sci. To-tal Environ., 393, 226–240, doi:10.1016/j.scitotenv.2007.12.030, 2008.
Hagler, G. S. W., Tang, W., Freeman, M. J., Heist, D. K., Perry, S. G., and Vette, A. F.: Model evaluation of roadside barrier im-pact on near-road air pollution, Atmos. Environ., 45, 2522–2530, doi:10.1016/j.atmosenv.2011.02.030, 2011.
Hargreaves, D. M., and Wright, N. G.: On the use of the k-epsilon model in commercial CFD software to model the neutral atmo-spheric boundary layer, J. Wind Eng. Ind. Aerodyn., 95, 355– 369, doi:10.1016/j.jweia.2006.08.002, 2007.
Jacobson, M. Z.: Fundamentals of atmospheric modeling, Cam-bridge University Press, 2005.
Karner, A. A., Eisinger, D. S., and Niemeier, D. A.: Near-Roadway Air Quality: Synthesizing the Findings from Real-World Data, Environ. Sci. Technol., 44, 5334–5344, doi:10.1021/es100008x, 2010.
Keskinen, J., and Ronkko, T.: Can Real-World Diesel Exhaust Particle Size Distribution be Reproduced in the Laboratory? A Critical Review, J. Air Waste Manage. Assoc., 60, 1245–1255, doi:10.3155/1047-3289.60.10.1245, 2010.
Ketzel, M., and Berkowicz, R.: Modelling the fate of ultrafine par-ticles from exhaust pipe to rural background: an analysis of time scales for dilution, coagulation and deposition, Atmos. Environ., 38, 2639–2652, doi:10.1016/j.atmosenv.2004.02.020, 2004. Kim, D. H., Gautam, M., and Gera, D.: On the prediction of
concen-tration variations in a dispersing heavy-duty truck exhaust plume using k-epsilon turbulent closure, Atmos. Environ., 35, 5267– 5275, doi:10.1016/s1352-2310(01)00339-9, 2001.
Kirchner, U., Scheer, V., Vogt, R., and Kagi, R.: TEM study on volatility and potential presence of solid cores in nucleation mode particles from diesel powered passenger cars, J. Aerosol. Sci., 40, 55–64, doi:10.1016/j.jaerosci.2008.08.002, 2009. Kuhn, T., Biswas, S., and Sioutas, C.: Diurnal and seasonal
char-acteristics of particle volatility and chemical composition in the vicinity of a light-duty vehicle freeway, Atmos. Environ., 39, 7154–7166, doi:10.1016/j.atmosenv.2005.08.025, 2005. Kulmala, M., Riipinen, I., Sipila, M., Manninen, H. E., Petaja,
T., Junninen, H., Dal Maso, M., Mordas, G., Mirme, A., Vana, M., Hirsikko, A., Laakso, L., Harrison, R. M., Hanson, I., Le-ung, C., Lehtinen, K. E. J., and Kerminen, V. M.: Toward direct measurement of atmospheric nucleation, Science, 318, 89–92, doi:10.1126/science.1144124, 2007.
Kumar, P., Fennell, P., Langley, D., and Britter, R.: Pseudo-simultaneous measurements for the vertical variation of coarse, fine and ultrafine particles in an urban street canyon, Atmos. Environ., 42, 4304–4319, doi:10.1016/j.atmosenv.2008.01.010, 2008.
Kumar, P., Garmory, A., Ketzel, M., Berkowicz, R., and Britter, R.: Comparative study of measured and modelled number concentra-tions of nanoparticles in an urban street canyon, Atmos. Environ., 43, 949–958, doi:10.1016/j.atmosenv.2008.10.025, 2009. Kumar, P., Pirjola, L., Ketzel, M., and Harrison, R. M.:
Nanoparticle emissions from 11 non-vehicle exhaust sources - A review, Atmos. Environ., 67, 252–277, doi:10.1016/j.atmosenv.2012.11.011, 2013.
Labovsky, J., and Jelemensky, L.: Verification of CFD pollution dis-persion modelling based on experimental data, J. Loss Prev. Pro-cess Ind., 24, 166–177, doi:10.1016/j.jlp.2010.12.005, 2011. Li, L., Li, X. F., Lin, B. R., and Zhu, Y. X.: Improved k-epsilon
two-equation turbulence model for canopy flow, Atmos. Environ., 40, 762–770, doi:10.1016/j.atmosenv.2005.10.010, 2006a.
Li, X. X., Liu, C. H., Leung, D. Y. C., and Lam, K. M.: Re-cent progress in CFD modelling of wind field and pollutant transport in street canyons, Atmos. Environ., 40, 5640–5658, doi:10.1016/j.atmosenv.2006.04.055, 2006b.
Liggio, J., Gordon, M., Smallwood, G., Li, S.-M., Stroud, C., Stae-bler, R., Lu, G., Lee, P., Taylor, B., and Brook, J. R.: Are Emis-sions of Black Carbon from Gasoline Vehicles Underestimated? Insights from Near and On-Road Measurements, Environ. Sci. Technol., 46, doi:10.1021/es2033845, 2012.
Mathis, U., Ristimaki, J., Mohr, M., Keskinen, J., Ntziachris-tos, L., Samaras, Z., and Mikkanen, P.: Sampling conditions for the measurement of nucleation mode particles in the ex-haust of a diesel vehicle, Aerosol Sci. Technol., 38, 1149–1160, doi:10.1080/027868290891497, 2004.
Mathissen, M., Scheer, V., Vogt, R., and Benter, T.: Inves-tigation on the potential generation of ultrafine particles from the tire-road interface, Atmos. Environ., 45, 6172–6179, doi:10.1016/j.atmosenv.2011.08.032, 2011.
Nikolova, I., Janssen, S., Vos, P., Vrancken, K., Mishra, V., and Berghmans, P.: Dispersion modelling of traffic induced ultra-fine particles in a street canyon in Antwerp, Belgium and com-parison with observations, Sci. Total Environ., 412, 336–343, doi:10.1016/j.scitotenv.2011.09.081, 2011a.
Nikolova, I., Janssen, S., Vrancken, K., Vos, P., Mishra, V., and Berghmans, P.: Size resolved ultrafine particles emission model – A continues size distribution approach, Sci. Total Environ., 409, 3492–3499, doi:10.1016/j.scitotenv.2011.05.015, 2011b. Ning, Z., Hudda, N., Daher, N., Kam, W., Herner, J., Kozawa,
K., Mara, S., and Sioutas, C.: Impact of roadside noise barriers on particle size distributions and pollutants con-centrations near freeways, Atmos. Environ., 44, 3118-3127, 10.1016/j.atmosenv.2010.05.033, 2010.
Pant, P., and Harrison, R. M.: Estimation of the contribution of road traffic emissions to particulate matter concentrations from field measurements: A review, Atmos. Environ., 77, 78–97, doi:10.1016/j.atmosenv.2013.04.028, 2013.
Parente, A., Gorle, C., van Beeck, J., and Benocci, C.: A Com-prehensive Modelling Approach for the Neutral Atmospheric Boundary Layer: Consistent Inflow Conditions, Wall Function and Turbulence Model, Bound.-Layer Meteor., 140, 411–428, doi:10.1007/s10546-011-9621-5, 2011a.
Parente, A., Gorle, C., van Beeck, J., and Benocci, C.: Improved kappa-epsilon model and wall function formulation for the RANS simulation of ABL flows, J. Wind Eng. Ind. Aerodyn., 99, 267–278, doi:10.1016/j.jweia.2010.12.017, 2011b.
Petroff, A. and Zhang, L.: Development and validation of a size-resolved particle dry deposition scheme for application in aerosol transport models, Geosci. Model Dev., 3, 753–769, doi:10.5194/gmd-3-753-2010, 2010.
labora-tory measurements in Finland, Atmos. Environ., 40, 867–879, doi:10.1016/j.atmosenv.2005.10.018, 2006.
Pontiggia, M., Derudi, M., Busini, V., and Rota, R.: Haz-ardous gas dispersion: A CFD model accounting for atmo-spheric stability classes, J. Hazard. Mater., 171, 739–747, doi:10.1016/j.jhazmat.2009.06.064, 2009.
Pye, H. O. T., and Seinfeld, J. H.: A global perspective on aerosol from low-volatility organic compounds, Atmos. Chem. Phys., 10, 4377–4401, doi:10.5194/acp-10-4377-2010, 2010.
Reponen, T., Grinshpun, S. A., Trakumas, S., Martuzevicius, D., Wang, Z. M., LeMasters, G., Lockey, J. E., and Biswas, P.: Con-centration gradient patterns of aerosol particles near interstate highways in the Greater Cincinnati airshed, J. Environ. Monit., 5, 557–562, doi:10.1039/b303557c, 2003.
Richards, P. J. and Hoxey, R. P.: Appropriate boundary-conditions for computational wind engineering models using the kappa-epsilon turbulence model, J. Wind Eng. Ind. Aerodyn., 46-7, 145–153, doi:10.1016/0167-6105(93)90124-7, 1993.
Riddle, A., Carruthers, D., Sharpe, A., McHugh, C., and Stocker, J.: Comparisons between FLUENT and ADMS for atmo-spheric dispersion modelling, Atmosp. Environ., 38, 1029–1038, doi:10.1016/j.atmosenv.2003.10.052, 2004.
Rodi, W.: Comparison of LES and RANS calculations of the flow around bluff bodies, J. Wind Eng. Ind. Aerodyn., 71, 55–75, 1997.
Ronkko, T., Virtanen, A., Kannosto, J., Keskinen, J., Lappi, M., and Pirjola, L.: Nucleation mode particles with a nonvolatile core in the exhaust of a heavy duty diesel vehicle, Environ. Sci. Technol., 41, 6384–6389, doi:10.1021/es0705339, 2007.
Ruckerl, R., Phipps, R. P., Schneider, A., Frampton, M., Cyrys, J., Oberdorster, G., Wichmann, H. E., and Peters, A.: Ultrafine par-ticles and platelet activation in patients with coronary heart dis-ease – results from a prospective panel study, Part Fibre Toxicol, 4, 1743–8977, 2007.
Schlesinger, R. B.: The health impact of common inorganic components of fine particulate matter (PM2.5) in ambi-ent air: A critical review, Inhal. Toxicol., 19, 811–832, doi:10.1080/08958370701402382, 2007.
Seigneur, C.: Current Understanding of Ultrafine Particulate Matter Emitted from Mobile Sources, J. Air Waste Manage. Assoc., 59, 3–17, doi:10.3155/1047-3289.59.1.3, 2009.
Sklavounos, S. and Rigas, F.: Validation of turbulence models in heavy gas dispersion over obstacles, J. Hazard. Mater., 108, 9– 20, doi:10.1016/j.jhazmat.2004.01.005, 2004.
Stroud, C. A., Liggio, J., Zhang, J., Gordon, M., Staebler, R. M., Makar, P. A., Zhang, J., Li, S. M., Mihele, C., and Lu, G.: Rapid Organic Aerosol Formation Downwind of a Highway: Measured and Model Results from the FEVER study, J. Geophys. Res. At-mos., 119, 1663–1679, doi:10.1002/2013JD020223, 2014. Tominaga, Y. and Stathopoulos, T.: Turbulent Schmidt numbers for
CFD analysis with various types of flowfield, Atmos. Environ., 41, 8091-8099, 10.1016/j.atmosenv.2007.06.054, 2007. Toner, S. M., Shields, L. G., Sodeman, D. A., and Prather, K.
A.: Using mass spectral source signatures to apportion exhaust particles from gasoline and diesel powered vehicles in a free-way study using UF-ATOFMS, Atmos. Environ., 42, 568–581, doi:10.1016/j.atmosenv.2007.08.005, 2008.
Uhrner, U., von Lowis, S., Vehkamaki, H., Wehner, B., Brasel, S., Hermann, M., Stratmann, F., Kulmala, M., and Wiedensohler, A.:
Dilution and aerosol dynamics within a diesel car exhaust plume – CFD simulations of on-road measurement conditions, Atmos. Environ., 41, 7440–7461, doi:10.1016/j.atmosenv.2007.05.057, 2007.
Uhrner, U., Zallinger, M., von Lowis, S., Vehkamaki, H., Wehner, B., Stratmann, F., and Wiedensohler, A.: Volatile Nanoparticle Formation and Growth within a Diluting Diesel Car Exhaust, J. Air Waste Manage. Assoc., 61, 399–408, doi:10.3155/1047-3289.61.4.399, 2011.
Valavanidis, A., Fiotakis, K., and Vlachogianni, T.: Airborne Par-ticulate Matter and Human Health: Toxicological Assessment and Importance of Size and Composition of Particles for Ox-idative Damage and Carcinogenic Mechanisms, J. Environ. Sci. Health Pt. C-Environ. Carcinog. Ecotoxicol. Rev., 26, 339–362, doi:10.1080/10590500802494538, 2008.
Vehkamaki, H., Kulmala, M., Lehtinen, K. E. J., and Noppel, M.: Modelling binary homogeneous nucleation of water-sulfuric acid vapours: Parameterisation for high temperature emissions, En-viron. Sci. Technol., 37, 3392–3398, doi:10.1021/es0263442, 2003.
Wang, Y. J. and Zhang, K. M.: Modeling Near-Road Air Quality Using a Computational Fluid Dynamics Model, CFD-VIT-RIT, Environ. Sci. Technol., 43, 7778–7783, doi:10.1021/es9014844, 2009.
Wang, Y. J. and Zhang, K. M.: Coupled turbulence and aerosol dynamics modeling of vehicle exhaust plumes using the CTAG model, Atmos. Environ., 59, 284–293, doi:10.1016/j.atmosenv.2012.04.062, 2012.
Wang, Y. J., Nguyen, M. T., Steffens, J. T., Tong, Z. M., Wang, Y. G., Hopke, P. K., and Zhang, K. M.: Modeling multi-scale aerosol dynamics and micro-environmental air quality near a large highway intersection using the CTAG model, Sci. Total En-viron., 443, 375–386, doi:10.1016/j.scitotenv.2012.10.102, 2013. Xie, Z. T., and Castro, I. P.: Efficient generation of inflow condi-tions for large eddy simulation of street-scale flows, Flow Turbul. Combust., 81, 449–470, doi:10.1007/s10494-008-9151-5, 2008. Zhang, C. X.: Numerical predictions of turbulent recirculating-flows with a kappa-epsilon-model, J. Wind Eng. Ind. Aerodyn., 51, 177–201, doi:10.1016/0167-6105(94)90003-5, 1994. Zhang, K. M., and Wexler, A. S.: Evolution of particle number
dis-tribution near roadways – Part I: analysis of aerosol dynamics and its implications for engine emission measurement, Atmos. Environ., 38, 6643–6653, doi:10.1016/j.atmosenv.2004.06.043, 2004.
Zhang, K. M., Wexler, A. S., Zhu, Y. F., Hinds, W. C., and Sioutas, C.: Evolution of particle number distribution near roadways, Part II: the “road-to-ambient” process, Atmos. Environ., 38, 6655– 6665, doi:10.1016/j.atmosenv.2004.06.044, 2004.
Zhang, L. M., Gong, S. L., Padro, J., and Barrie, L.: A size-segregated particle dry deposition scheme for an at-mospheric aerosol module, Atmos. Environ., 35, 549–560, doi:10.1016/s1352-2310(00)00326-5, 2001.
Zhou, Y. and Levy, J. I.: Factors influencing the spatial extent of mobile source air pollution impacts: a meta-analysis, BMC Pub-lic Health, 7, 11, doi:10.1186/1471-2458-7-89, 2007.
Zhu, Y. F., Hinds, W. C., Kim, S., and Sioutas, C.: Concentration and size distribution of ultrafine particles near a major highway, J. Air Waste Manage. Assoc., 52, 1032–1042, 2002b.
